About the Project
This project is
completely made up and looks to examine whether coffee consumed has any
bearing on words written per day in PhD students.
Hopefully by the
end of this you get an idea of what flexdashboards can do and whether
you need more coffee.
Here is a picture that is embedded in the
document. This guy makes a great coffee
Lets have a look at the data visually. To do this we can embed a couple of plots and a filter for our data
The filter has some downsides compared to Shiny. When you open the
html file everything is displayed and there is no option for a default.
The All function doesn’t function either so you can make select multiple
available.
Other options if you don’t love this is to just use the interactivity
of the plotly object.
On this page we also have a tab structure so you can flick through
different plots.
You can also make a filter that is a slider
Note that the slider obliterates the boxplot because it doesn’t have an element that depends on “Day”
To answer this question we might want to fit a mixed model to our
data.
Using lme4 we fit this model
We can output some model information directly into the dashboard
Estimate Std. Error t value
(Intercept) 329.35220 67.21533 4.8999567
Coffee 13.28772 19.79733 0.6711874
$Student
(Intercept) Coffee
Student 1 453.0706 38.645517
Student 2 287.3432 4.677394
Student 3 466.2649 41.349876
Student 4 203.7830 -12.449426
Student 5 236.2993 -5.784757
attr(,"class")
[1] "coef.mer"
We might also like to make a plot of our model
As we can see there is a potential positive benefit for having more coffees when wanting to write more words. If in doubt have more coffee.